Построение модели прогнозирования матчей по статистике

Building a match prediction model based on statistics

Contenidos

How the sports match prediction model works based on statistics

The sports match prediction model is a mathematical algorithm that assesses the probabilities of outcomes based on historical and current statistics: home win, draw, away win, totals, handicaps, and other markets. The key to the accuracy of such models is a complete, detailed, and stable flow of data. This is provided by por el API de eventos deportivos api-sport.ru, where both pre-match and live statistics are available for football, basketball, tennis, hockey, table tennis, and esports.

Schematic representation of the process looks like this: first, you obtain the match history, lineups, statistics on shots, possession, duels, xG-like metrics (through groups in estadísticasDelPartido), as well as bookmaker odds from the field oddsBase. Then this raw data is cleaned, aggregated by teams and players, turning into numerical features — form, attacking and defensive strength, moment efficiency, home field advantage. Based on the prepared features, a machine learning algorithm is trained to predict outcomes based on similar historical situations.

At the application stage, the model uses fresh match data that you retrieve in real time from endpoints /v2/{sportSlug}/partidos и /v2/{sportSlug}/matches/{matchId}. Live fields like minutoDelPartidoActual, eventosEnVivo and detailed statistics by periods allow building in-play prediction models that take into account the dynamics during the game. In upcoming releases, the API plans to support WebSocket connections and built-in AI tools, which will make such models even more responsive.

Example of obtaining historical matches for training the model

import requests
API_KEY = 'ВАШ_API_КЛЮЧ'
BASE_URL = 'https://api.api-sport.ru/v2/football/matches'
headers = {'Authorization': API_KEY}
params = {
    'date': '2025-09-03',
    'status': 'finished'
}
response = requests.get(BASE_URL, headers=headers, params=params)
data = response.json()
matches = data.get('matches', [])
print('Загружено матчей:', len(matches))

The best sports statistics APIs for building match predictions

For professional prediction models, it requires not just «score and result,» but a deep slice of data for each event: tournament structure, lineups, player characteristics, advanced statistics, and bookmaker odds. This is exactly the set provided by Sport Events API at api-sport.ru, covering major global sports and constantly adding new leagues and disciplines.

Through a single API, you get a list of sports by method /v2/deporte, followed by categories of countries and tournaments (endpoints /v2/{sportSlug}/categorías и /v2/{sportSlug}/categorías/{categoryId}), then – seasons and matches. For each match, key fields are available: exact start time, statuses, score by periods, lineups with detailed structure, a live events array, and a rich block estadísticasDelPartido with dozens of metrics on shots, duels, passes, defensive actions, and goalkeeping. All this creates a foundation for complex models of team strength, expected goals, totals, and handicaps.

A separate competitive advantage is the presence in matches of a block oddsBase with markets and bookmaker odds. This allows not only to predict outcome probabilities but also to immediately calculate the value of bets and simulate ROI strategies. The API is designed to be convenient for both pre-match models (based on historical data) and live analytics, which can eventually be built on top of WebSocket subscriptions for updates.

Example of obtaining a list of sports through the API

import requests
API_KEY = 'ВАШ_API_КЛЮЧ'
BASE_URL = 'https://api.api-sport.ru/v2/sport'
headers = {'Authorization': API_KEY}
response = requests.get(BASE_URL, headers=headers)
sports = response.json()
for sport in sports:
    print(sport['id'], sport['translations'].get('ru', sport['name']))

How to obtain match data via API and prepare a sample for the model

The first practical step in building a forecasting model is forming a training sample. In the case of the Sport Events API, you start by obtaining arrays of matches for a specific sport. The endpoint /v2/{sportSlug}/partidos allows filtering data by dates, tournaments, seasons, statuses, and teams. For example, for football, you can gather all completed matches of a specific league over several seasons and use them as a training base.

After exporting the data, it is important to bring it to an analytical format: highlight the target variable (outcome 1X2, total, goal difference), form features for the team and opponent (home/away, form over the last N matches, average metrics for shots, possession, xG-like metrics, disciplinary indicators). It is also useful to include odds from oddsBase as market «wisdom of the crowd,» which often increases the accuracy of models. All these features are calculated at the time of the match to avoid information leakage from the future.

For practical work, it is enough to register and obtain a key in the personal account. app.api-sport.ru, after which you can automate data collection with scripts and periodically update the dataset on a schedule. In the future, the same sampling logic will be useful for forming an online feature feed that will feed the already trained model.

Example of sampling completed matches of the tournament

import requests
API_KEY = 'ВАШ_API_КЛЮЧ'
SPORT = 'football'
BASE_URL = f'https://api.api-sport.ru/v2/{SPORT}/matches'
headers = {'Authorization': API_KEY}
params = {
    'tournament_id': '25182,77142',  # несколько турниров через запятую
    'status': 'finished'
}
response = requests.get(BASE_URL, headers=headers, params=params)
data = response.json()
matches = data.get('matches', [])
# здесь можно преобразовать matches в pandas.DataFrame и построить признаки

Which algorithms to use for predicting sports events

The choice of algorithm depends on the task and the volume of data, but in practice, proven approaches from classical machine learning and statistics are often used. Logistic regression, gradient boosting (XGBoost, LightGBM, CatBoost), and random forests work well for predicting outcomes 1X2 or totals. These methods are robust to heterogeneous features, allow for nonlinear dependencies, and provide interpretable feature importances, which is important when analyzing the impact of specific statistical metrics from estadísticasDelPartido.

For tasks predicting exact scores or the number of goals, Poisson models and their extensions are often used, where the goal intensities of teams depend on attacking and defensive strength, form, home factor, and other features. In this case, access to a long history of matches through /v2/{sportSlug}/partidos, allows for stable estimation of such model parameters. When working with live data (fields minutoDelPartidoActual, eventosEnVivo), temporal models can be built: updating outcome probabilities as new information about possession, shots, and cards comes in.

With a large volume of data and the need to model complex interactions between features, neural networks are used: from simple multilayer perceptrons to recurrent and temporal CNN architectures for sequences of match events. In the future, it is planned to launch built-in AI services based on the same dataset in the infrastructure of api-sport.ru, which will simplify the integration of advanced models for users without deep knowledge in machine learning.

Example of basic logistic regression for outcome 1X2

from sklearn.linear_model import LogisticRegression
# X — матрица признаков (статистика команд до матча)
# y — целевая переменная (0 — гости, 1 — ничья, 2 — хозяева)
model = LogisticRegression(max_iter=1000, multi_class='multinomial')
model.fit(X_train, y_train)
probas = model.predict_proba(X_test)
print('Вероятности исходов для первого матча:', probas[0])

How to train a match prediction model and evaluate its accuracy

Correct training and evaluation of the model is key to ensuring that predictions work with real money, not just on paper. For time series, which are essentially sports data, it is important to maintain chronology: training the model on older seasons and testing it on newer ones. Historical matches obtained through /v2/{sportSlug}/partidos, can be conveniently divided by date or season, forming training, validation, and test slices without overlap.

For quality metrics in betting analytics, in addition to the standard accuracy and F1 for classification tasks, logloss and Brier score are often used, which assess the calibration of probabilities. If you use a block oddsBase with bookmaker coefficients, you can additionally calculate financial metrics: average ROI, maximum drawdown, and bank volatility over the test period. Such a «backtest» allows you to evaluate not only the mathematical accuracy of the model but also the practical applicability of its signals.

The training process usually includes several iterations: a base model, error analysis, adding new features (for example, extended metrics from estadísticasDelPartido or team strength ratings), hyperparameter tuning, and final training on the entire historical sample. All of this can be easily automated in a pipeline that updates data via API on a schedule, retrains the model, and recalculates metrics. This model can then be connected to a production API service to provide real-time forecasts.

Example of calculating model quality metrics

from sklearn.metrics import accuracy_score, log_loss
# y_test — реальные исходы, y_proba — предсказанные вероятности классов
accuracy = accuracy_score(y_test, y_pred)
ll = log_loss(y_test, y_proba)
print('Accuracy:', round(accuracy, 3))
print('Log-loss:', round(ll, 3))

How to connect the prediction model to the API and automate predictions

When the model is trained and tested, the next step is integration into production. A typical architecture looks like this: a separate service (microservice) with the model periodically requests fresh match data via the Sport Events API, forms features, and returns outcome probabilities and value bet estimates. Match and odds data are loaded from endpoints /v2/{sportSlug}/partidos и /v2/{sportSlug}/matches/{matchId}, where the field oddsBase provides a complete picture of the 1X2 markets, totals, handicaps, and other bets.

Automation can be implemented on a schedule (cron job script launches), triggered by updates from bookmakers, or in real-time via a WebSocket connection to sports data streams (functionality that will soon appear in the api-sport.ru ecosystem). In this mode, your service will respond to every change in statistics or odds and update forecasts almost instantly, which is especially important for live betting and trading.

To minimize the time to production, it is convenient to use the same API client for preparing the training sample and for the production forecasting service. It is enough to change the environment and access key from the dashboard. app.api-sport.ru, configure request logging, cache the most frequent data, and monitor the quality of predictions. On top of this, dashboards, alerts, and automated betting strategies can be built using bookmakers’ APIs and the expanding AI capabilities of the platform.

An example of a service that requests matches and calculates predictions.

import requests
API_KEY = 'ВАШ_API_КЛЮЧ'
SPORT = 'football'
BASE_URL = f'https://api.api-sport.ru/v2/{SPORT}/matches'
headers = {'Authorization': API_KEY}
# model.predict_proba должен быть реализован заранее
def get_today_predictions(model):
    params = {'status': 'notstarted'}
    response = requests.get(BASE_URL, headers=headers, params=params)
    data = response.json()
    predictions = []
    for match in data.get('matches', []):
        features = build_features_from_match(match)  # ваша функция построения признаков
        proba = model.predict_proba([features])[0]
        predictions.append({
            'match_id': match['id'],
            'proba_home': proba[2],
            'proba_draw': proba[1],
            'proba_away': proba[0]
        })
    return predictions

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